Compared with inductive few-shot learning methods, transductive few-shot learning methods aim to explore how to utilize the query set to help improve the performance of image classification.
The first type is GNN-based methods that enhance classification performance by exploiting Graph Neural Networks (GNN) in the transductive few-shot learning task.
Typical works include TPN [1]}, EGNN [2]} and DPGN [3]}.
Some other methods that use query instances to refine prototypes seem more straightforward.
For example, CAN [4]} adopts an iterative process during transductive inference. Specifically, in each iteration, the top-k confident query instances are chosen and used to update the prototypes. The uncertainty confidence for each query instance equals the cosine similarity between itself and the prototype.
MCT [5]} also adopts the iterative process, but it attempts to adapt the confidence to a specific task by adding an extra learnable temperature to the Euclidean distance metric.
